Copy_of_RNNTinderProfileGeneration.ipynb

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      "name": "Copy of RNNTinderProfileGeneration.ipynb",
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      "source": [
        "# Tinder profile generation with RNNs"
      ]
    },
    {
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        "\n",
        "\n",
        "Part of the Tinder profile generation project\n",
        "This section handles the profile text generation using LSTMs\n",
        "we use the kaggle TInder dataset at https://www.kaggle.com/immune/tinder-profiles\n",
        "\n",
        "\n",
        "Based primary on Brownlees tutorial\n",
        "https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/\n",
        "@author: Adrian\n",
        "\n",
        "DONT FORGET TO GET RANDOM SEED GEN FROM TRUMP OR BROWNLEE\n",
        "\n",
        "Brownlees tutorial helps with understanding, but for actual generation, Trump CharRNN is much more useful\n",
        "https://github.com/jctestud/char-rnn/blob/master/char-rnn_trump_text_generation-TRAINING.ipynb\n",
        "\n",
        "TLDR: Uses a certain length of chars to predict the next char in a Bio, trained on over 40K Bios"
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "#Import all packages\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import string\n",
        "import warnings\n",
        "import sklearn\n",
        "import io\n",
        "import scipy\n",
        "import numpy\n",
        "import json\n",
        "import nltk\n",
        "import sys\n",
        "import csv\n",
        "import os\n",
        "import re\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dropout, Activation, Dense\n",
        "\n",
        "!gdown https://drive.google.com/uc?id=1G5ud0F1DYut4Vcd6WSPrS-CtD8notTSd\n"
      ],
      "execution_count": 0,
      "outputs": [
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          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1G5ud0F1DYut4Vcd6WSPrS-CtD8notTSd\n",
            "To: /content/tinder_data_final.xlsx\n",
            "\r0.00B [00:00, ?B/s]\r3.75MB [00:00, 119MB/s]\n"
          ],
          "name": "stdout"
        }
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        "id": "7OdDzt7wpwUH",
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        "outputId": "bcec9f2c-a040-4cc0-c8b7-38d8348d03b0",
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      "source": [
        "#Text import and preprocessing\n",
        "base_dir = 'tinder_data_final.xlsx'\n",
        "\n",
        "\n",
        "tinder_profile_df = pd.read_excel(base_dir, error_bad_lines=False, delimiter='\\t')\n",
        "\n",
        "# Print dataframe info\n",
        "tinder_profile_df.info()\n",
        "\n",
        "print(tinder_profile_df.head())\n",
        "\n",
        "# This will print the number of rows and columns that will be used to train\n",
        "print(\"Shape of train set : \",tinder_profile_df.shape)\n",
        "\n",
        "#tinder_profile_df.head()\n",
        "\n",
        "#Convert dataframe to string already here\n",
        "\n",
        "tinder_bios_df=tinder_profile_df['Unnamed: 2'].apply(str)\n",
        "\n",
        "\n",
        "\n",
        "#Print only thhe bios\n",
        "#print(tinder_bios_df)\n",
        "\n",
        "#Define a generic vocabulary\n",
        "# generic vocabulary\n",
        "characters = list(string.printable)\n",
        "characters.remove('\\x0b')\n",
        "characters.remove('\\x0c')\n",
        "\n",
        "VOCABULARY_SIZE = len(characters)\n",
        "characters_to_ix = {c:i for i,c in enumerate(characters)}\n",
        "print(\"vocabulary len = %d\" % VOCABULARY_SIZE)\n",
        "print(characters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 60042 entries, 0 to 60041\n",
            "Data columns (total 4 columns):\n",
            "5b56c7177d34e24c5c74db22    60042 non-null object\n",
            "Jany                        60040 non-null object\n",
            "Unnamed: 2                  43660 non-null object\n",
            "1991-07-29T16:53:27.285Z    60027 non-null object\n",
            "dtypes: object(4)\n",
            "memory usage: 1.8+ MB\n",
            "   5b56c7177d34e24c5c74db22     Jany  \\\n",
            "0  5a7bc2c82fb5fcca0d3243ef   Brenda   \n",
            "1  58941a8708ba58e05c2c1d25  Katelyn   \n",
            "2  5b57f69f7c7c6fca6711513f       Ce   \n",
            "3  5b55740580c1c36505eb7dfe      Mel   \n",
            "4  58ba472d208d289b6a1fb406    Mandy   \n",
            "\n",
            "                                          Unnamed: 2  1991-07-29T16:53:27.285Z  \n",
            "0                                                NaN  1993-07-29T16:53:27.285Z  \n",
            "1  I'm just here to meet your dog or cat Snapchat...  1999-07-29T16:53:27.285Z  \n",
            "2  Me gusta la pizza y lo único que busco aquí so...  1999-07-29T16:53:27.285Z  \n",
            "3  I’m just a happy hippie. Peace man ✌🏽 It’s all...  1992-07-29T16:53:27.285Z  \n",
            "4  30 single been separated for 2yrs been battlin...  1987-07-29T16:53:27.285Z  \n",
            "Shape of train set :  (60042, 4)\n",
            "vocabulary len = 98\n",
            "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\\\', ']', '^', '_', '`', '{', '|', '}', '~', ' ', '\\t', '\\n', '\\r']\n"
          ],
          "name": "stdout"
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      "source": [
        "#Extract individual bios into one large string\n",
        "\n",
        "#Usually We would pad our sequences, but a quick inspection reveals sequences are drastically different lenghts\n",
        "#hence lets just join them together.\n",
        "#We define a filtering function first\n",
        "holder = []\n",
        "for i in range(0, len(tinder_bios_df)):\n",
        "    #print (tinder_bios_df.iloc[i]);\n",
        "    string = tinder_bios_df.iloc[i]\n",
        "\n",
        "    if(string== \"nan\"):\n",
        "        print(\"nan removed\")\n",
        "    else:\n",
        "        holder.append(string)\n",
        "\n",
        "#print(holder) \n",
        "\n",
        "alphaholder= []\n",
        "regex = re.compile('[^A-Za-z0-9 -.,]')\n",
        "#now that holder is ready we remove all non alphanumeric entries\n",
        "for i in range(0, len(holder)):\n",
        "    string = holder[i]\n",
        "    \n",
        "    \n",
        "    #First parameter is the replacement, second parameter is your input string\n",
        "    newstring = regex.sub('', string)\n",
        "\n",
        "    alphaholder.append(newstring)\n",
        "    \n",
        "    \n",
        "#print(alphaholder)\n",
        "\n",
        "#Split data into train and validation strings\n",
        "trainholder=[]\n",
        "validationholder=[]\n",
        "\n",
        "print(len(alphaholder))\n",
        "#Len alphaholder is 43660\n",
        "for i in range(0, 34928): \n",
        "    trainstring = alphaholder[i]\n",
        "    trainholder.append(trainstring)\n",
        "for i in range(34928, len(alphaholder)): \n",
        "    valstring = alphaholder[i]\n",
        "    validationholder.append(valstring)\n",
        "\n",
        "\n",
        "#14-04-2019 Data is now ready, need to append into giant string\n",
        "finalstring = ''.join(trainholder)\n",
        "validationstring = ''.join(validationholder)\n",
        "\n",
        "print(finalstring)\n",
        "print(validationstring)"
      ],
      "execution_count": 0,
      "outputs": [
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          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n",
            "\n",
            "Current values:\n",
            "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
            "NotebookApp.rate_limit_window=3.0 (secs)\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "i1GbKdFYqO41",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Model Building**"
      ]
    },
    {
      "metadata": {
        "id": "7x3qGOqAqSul",
        "colab_type": "code",
        "outputId": "ac9eb844-9ded-4283-bfd6-69e0c4738d22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "cell_type": "code",
      "source": [
        "#Some variables to set here\n",
        "N_GPU = 1\n",
        "# you can experiment with more GPUs, it gets interesting with a high SEQUENCE_LEN\n",
        "SEQUENCE_LEN = 20; \n",
        "#This is the char length of each individual sequence\n",
        "#Essentially, we use 60 chars to predict the 61st char. This needs to be shorter for us\n",
        "BATCH_SIZE = 512 #Number of sequences fed at once in batch\n",
        "EPOCHS = 13\n",
        "HIDDEN_LAYERS_DIM = 512\n",
        "LAYER_COUNT = 4\n",
        "DROPOUT = 0.2\n",
        "\n",
        "text_train_len = len(finalstring)\n",
        "text_val_len = len(validationstring)\n",
        "def describe_batch(X, y, samples=3):\n",
        "    \"\"\"Describe in a human-readable format some samples from a batch.  Show the next char given previous char\"\"\"\n",
        "    for i in range(samples):\n",
        "        sentence = \"\"\n",
        "        for s in range(SEQUENCE_LEN):\n",
        "            sentence += characters[X[i,s,:].argmax()]\n",
        "        next_char = characters[y[i,:].argmax()]\n",
        "        \n",
        "        print(\"sample #%d: ...%s -> '%s'\" % (\n",
        "            i,\n",
        "            sentence[-20:],\n",
        "            next_char\n",
        "        ))\n",
        "    \n",
        "def batch_generator(text, count):\n",
        "    \"\"\"Generate batches for training\"\"\"\n",
        "    while True: # keras wants that for reasons\n",
        "        for batch_ix in range(count):\n",
        "            X = np.zeros((BATCH_SIZE, SEQUENCE_LEN, VOCABULARY_SIZE))\n",
        "            y = np.zeros((BATCH_SIZE, VOCABULARY_SIZE))\n",
        "\n",
        "            batch_offset = BATCH_SIZE * batch_ix\n",
        "\n",
        "            for sample_ix in range(BATCH_SIZE):\n",
        "                sample_start = batch_offset + sample_ix\n",
        "                for s in range(SEQUENCE_LEN):\n",
        "                    X[sample_ix, s, characters_to_ix[text[sample_start+s]]] = 1\n",
        "                y[sample_ix, characters_to_ix[text[sample_start+s+1]]]=1\n",
        "\n",
        "            yield X, y\n",
        "            \n",
        "\n",
        "\n",
        "for ix, (X,y) in enumerate(batch_generator(finalstring, count=1)):\n",
        "    # describe some samples from the first batch\n",
        "    describe_batch(X, y, samples=5)\n",
        "    break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample #0: ...I'm just here to mee -> 't'\n",
            "sample #1: ...'m just here to meet -> ' '\n",
            "sample #2: ...m just here to meet  -> 'y'\n",
            "sample #3: ... just here to meet y -> 'o'\n",
            "sample #4: ...just here to meet yo -> 'u'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "QD7I3Td-qb0q",
        "colab_type": "code",
        "outputId": "9fb21856-a603-4eff-9f64-8f6d34c04832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "###########################################Model Building#################################\n",
        "    ###From trump char builder\n",
        "def build_model(gpu_count=1):\n",
        "    \"\"\"Build a Keras sequential model for training the char-rnn\"\"\"\n",
        "    model = Sequential()\n",
        "    for i in range(LAYER_COUNT):\n",
        "        model.add(\n",
        "            LSTM(\n",
        "                HIDDEN_LAYERS_DIM, \n",
        "                return_sequences=True if (i!=(LAYER_COUNT-1)) else False,\n",
        "                input_shape=(SEQUENCE_LEN, VOCABULARY_SIZE),\n",
        "            )\n",
        "        )\n",
        "        model.add(Dropout(DROPOUT))\n",
        "    \n",
        "    model.add(Dense(VOCABULARY_SIZE))\n",
        "    model.add(Activation('softmax'))\n",
        "    \n",
        "    #removed the multigpu line\n",
        "    \n",
        "    model.compile(loss='categorical_crossentropy', optimizer=\"adam\")\n",
        "    return model\n",
        "\n",
        "\n",
        "training_model = build_model(gpu_count=N_GPU)\n",
        "\n",
        "train_batch_count = (text_train_len - SEQUENCE_LEN) // BATCH_SIZE\n",
        "val_batch_count = (text_val_len - SEQUENCE_LEN) // BATCH_SIZE\n",
        "print(\"training batch count: %d\" % train_batch_count)\n",
        "print(\"validation batch count: %d\" % val_batch_count)\n",
        "\n",
        "\n",
        "#NO CHECKPOINTING IN THIS VERSION, because of simplicity and because our dataset is much smaller"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training batch count: 8735\n",
            "validation batch count: 2642\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IFc8403oqhrd",
        "colab_type": "code",
        "outputId": "f0943754-93d0-4a39-d722-01680848a1ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "cell_type": "code",
      "source": [
        "history = training_model.fit_generator(\n",
        "    batch_generator(finalstring, count=train_batch_count),\n",
        "    train_batch_count,\n",
        "    max_queue_size=1, # no more than one queued batch in RAM\n",
        "    epochs=EPOCHS,\n",
        "    validation_data=batch_generator(validationstring, count=val_batch_count),\n",
        "    validation_steps=val_batch_count,\n",
        "    initial_epoch=0\n",
        ")\n",
        "training_model.save('basic_LSTM_tindergenv1_4layers_30char.h5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/13\n",
            "8735/8735 [==============================] - 2298s 263ms/step - loss: 1.6185 - val_loss: 1.1585\n",
            "Epoch 2/13\n",
            "8735/8735 [==============================] - 2293s 262ms/step - loss: 1.2694 - val_loss: 1.0225\n",
            "Epoch 3/13\n",
            "8735/8735 [==============================] - 2294s 263ms/step - loss: 1.2076 - val_loss: 0.9630\n",
            "Epoch 4/13\n",
            "8735/8735 [==============================] - 2300s 263ms/step - loss: 1.1772 - val_loss: 0.9347\n",
            "Epoch 5/13\n",
            "8735/8735 [==============================] - 2298s 263ms/step - loss: 1.1602 - val_loss: 0.9239\n",
            "Epoch 6/13\n",
            "8735/8735 [==============================] - 2298s 263ms/step - loss: 1.1504 - val_loss: 0.9145\n",
            "Epoch 7/13\n",
            "8735/8735 [==============================] - 2299s 263ms/step - loss: 1.1446 - val_loss: 0.9116\n",
            "Epoch 8/13\n",
            " 726/8735 [=>............................] - ETA: 31:35 - loss: 1.4293Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cgFxYvt0tuz7",
        "colab_type": "code",
        "outputId": "2a24e1b1-b146-4d92-9f33-03b955fa4504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "cell_type": "code",
      "source": [
        "#############################################################################################################3\n",
        "###After this we can use the trained model to predict things, and view history variables\n",
        "\n",
        "##Plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HxD3vGXstL5SUQGkp3HwzPPoo9OsH//3f2Y5KRPJdOpL708BFsVEzxwHb3H1D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\nIhJBSu4iIhH0/wHfonlkEZJtWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}